New 2D pupil and spot center positioning technology under real — Time eye tracking

Jiancheng Zou, Honggen Zhang, Tengfan Weng
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Abstract

Eye tracking technology is an important technology in the field of artificial intelligence(AI). Eye tracking will promote the development of human-computer interaction(HCI). Spot Center Corneal Reflex (PCCR) is an eye tracking technique that relies on pupils and reflected light spots. Therefore, it is significant to accurately locate the pupil position and reflected spot position. The traditional algorithm used the edge and the gray information of the image to extract the contours of the pupil and the spot, and then determine the location through the fitting. However, the collected images will be affected by many environmental factors, the boundary point and the fitting calculation will greatly affect the efficiency and stability of the algorithm. In this paper, a new method combining image gradient information with threshold segmentation is proposed. Gradient detection and threshold segmentation are carried out in the region of interest, and the pupil and reflection spot are extracted directly. So, this paper use the centroid method to calculate the center coordinates more accurately. The algorithm has a good robust performance to avoid noise and environmental effects. The algorithm used to develop human eye tracking system to achieve real-time eye tracking, while ensuring accuracy.
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实时眼动追踪下新的二维瞳孔和光斑中心定位技术
眼动追踪技术是人工智能领域的一项重要技术。眼动追踪技术将促进人机交互技术的发展。点中心角膜反射(PCCR)是一种依靠瞳孔和反射光点的眼球追踪技术。因此,准确定位瞳孔位置和反射光斑位置具有重要意义。传统算法利用图像的边缘和灰度信息提取瞳孔和斑点的轮廓,然后通过拟合确定位置。然而,采集到的图像会受到许多环境因素的影响,边界点和拟合计算将极大地影响算法的效率和稳定性。本文提出了一种将图像梯度信息与阈值分割相结合的新方法。在感兴趣区域进行梯度检测和阈值分割,直接提取瞳孔和反射斑。因此,本文采用质心法更精确地计算中心坐标。该算法具有良好的鲁棒性,可以避免噪声和环境影响。该算法用于开发人体眼动追踪系统,实现实时眼动追踪,同时保证准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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